Applied Longitudinal
Data Analysis
Course Description
This course provides an overview of modern statistical methods for analyzing repeated/longitudinal data. We will introduce parametric (generalized linear mixed models, or GLMM), semi-parametric (generalized estimating equations, or GEE), and nonparametric (identification of longitudinal trajectory profiles) methods for the analysis of longitudinal data. We will also contrast these strategies with parametric methods for autoregressive time series analysis. Emphasis will be given to clinical research applications, with labs covering coding examples using the statistical software R. ​​
Instructor
Tanayott Thaweethai, PhD
Associate Director, Biostatistics Research and Engagement,
Massachusetts General Hospital
Assistant Professor of Medicine, Biostatistics, Harvard Medical School
Tanayott (Tony) Thaweethai, PhD is the Associate Director of Biostatistics Research and Engagement at Massachusetts General Hospital Biostatistics, an Assistant Professor of Medicine at Harvard Medical School, and an Assistant Professor in the Department of Biostatistics at Harvard T.H. Chan School of Public Health. He has taught the course “Applied Regression Analysis” every year at Harvard Chan since 2023, which covers regression model building and interpretation.  He has also lectured nationally and internationally regarding his statistical research, which focuses on missing data in observational studies, as well as his clinical research, which include diabetes and Long COVID. His work has been published in  JAMA, Nature Communications,  Annals of Internal Medicine,  JAMA Pediatrics, JAMA Network Open, and other high-impact journals.